Transcript Inference in First Order Logic

Inference in First
Order Logic
Outline
Reducing first order inference to propositional
inference
 Unification
 Generalized Modus Ponens
 Forward and backward chaining
 Logic programming
 Resolution
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A brief history of reasoning
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450 B.C. Stoics: propositional logic, inference (maybe).
322 B.C. Aristotle: “syllogisms” (inference rules), quantifiers
1565 Cardano: probability theory (propositional logic + uncertainty)
1847 Boole: propositional logic (again)
1879 Frege: first-order logic
1922 Wittgenstein: proof by truth tables
1930 Gödel:
complete algorithm for FOL
1930 Herbrand: complete algorithm for FOL (reduce to propositional
1931 Gödel:
complete algorithm for arithmetic
1960 Davis/Putnam: “practical” algorithm for propositional logic
1965 Robinson: “practical” algorithm for FOL-resolution
Universal Instantiation (UI)
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Every instantiation of a universally quantified sentence is
entailed by it:
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for any variable v and ground term g
E.g.,
yields
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Existential Instantiation (EI)
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For any sentence α, variable v, and constant symbol k that does not
appear elsewhere in the knowledge base:
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E.g.,
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provided C1 is a new constant symbol, called a Skolem constant
Another example: from
we obtain
provided e is a new constant symbol.
yields
Universal Instantiation and
Existential Instantiation
Universal Instantiation (UI) can be applied several
times to add new sentences:
the new KB is logically equivalent to the old.
 Existential Instantiation can be applied once to
replace the existential sentence:
the new KB is not equivalent to the old, but is
satisfiable if and only if the old KB is satisfiable.
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Reduction to Propositional
Inference
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Suppose the KB contains just the following:
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Instantiating the universal sentence in all possible ways, we have
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The new KB is propositionalized: proposition symbols are
Reduction contd.
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Claim: a ground sentence* is entailed by new KB iff entailed by original
KB
Claim: every FOL KB can be propositionalized so as to preserver
entailment.
Idea: propositionalize KB and query, apply resolution, return result.
Problem: with function symbols, there are infinitely many ground terms,
e.g., Father(Father(Father(John)))
Theorem: Herbrand (1930). If a sentence α is entailed by an FOL KB, it
is entailed by a finite subset a propositional KB.
Idea: For n = 0 to ∞ do
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create a propositional KB by instantiating with depth-n terms
see if α is entailed by this KB
Problem: works if α is entailed, loops if α is not entailed
Theorem: Turing (1936), Church (1936), entailement in FOL is
semidecidable
Problems with
propositionalization
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Propositionalization seems to generate lots of irrelevant
sentences. E.g., from
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it seems obvious that Evil(John), but propositionalization
produce lots of facts such as Greedy(Richard) that are
irrelevant.
With p k-ary predicates and n constants, there are p – nk
instantiations!
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Unification
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We can get the inference immediately if we can find a
substitution φ such that King(x) and Greedy(x) match
King(John) and Greedy(y)
φ = {x/John, y/John} works
UNIFY(α,ß) = φ if αφ = ßφ
Standardizing apart eliminates overlap of variables, e.g.,
Knows(z17, OJ)
Generalized Modus Ponens
(GMP)
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GMP used with KB of definite clauses (exactly one positive
literal
All variables assumed universally quantified.
Soundness of GMP
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Need to show that
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provided that
for all i
Lemma: For any definite clause p, we have
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by UI
Example knowledge base
The law says that it is a crime for an American to sell
weapons to hostile nations. The country Nono, an
enemy America, has some missiles, and all of its
missiles were sold to it by Col. West who is American
 Prove that Col. West is a criminal
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Knowledge base contd.
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It is a crime for an American to sell weapons to hostile nations:
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Nono … has some missiles, i.e.,
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… all of it missiles were sold to it by Col. West
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Missiles are weapons
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An enemy of America counts as “hostile”
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West who is American …
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The country Nono, an enemy of America …
Forward chaining algorithm
Properties of forward chaining
Sound and complete for first-order definite clauses
(proof similar to propositional proof)
 Datalog = first-order definite clauses + no functions
(e.g., crime KB) FC
 FC terminates for Datalog in poly iterations: at most
pnk literals.
 May not terminate in general if α is not entailed.
 This is unavoidable: entailment with definite clauses
semidecidable
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Efficiency of forward chaining
Simple observation: no need to match a rule on
iteration k if a premise wasn’t added on iteration k – 1
 match each rule whose premise contains a newly
added literal.
 Matching itself can be expensive.
 Database indexing allows O(1) retrieval of known
facts, e.g., query Missile(x) retrieves Missile(M1).
 Matching conjunctive premises against known facts
is NP-hard.
 FC is widely used in deductive database
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Backward chaining algorithm
Properties of backward
chaining
Depth-first recursive proof search: space is linear in
size of proof.
 Incomplete due to infinite loops  fix by checking
current goal against every goal on stack.
 Inefficient due to repeated subgoals (both success and
failure)  fix using caching of previous results
(extra space!).
 Widely used for logic programming
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Logic programming
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Computation as inference on logical KBs
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Should be easier to debug Capitali(NewYork, USA) than x := x
+ 2!
Resolution: brief summary
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Full first-order version:
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where
For example,
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With
Apply resolution steps to
; complete for FOL